Bobby Davis

Papers

Total Citations

H-Index

About

Bobby Davis is a robotics researcher whose work focuses on motion planning under uncertainty, with a particular emphasis on coverage-aware trajectory optimization and human-robot interaction. His most cited paper, "C-OPT: Coverage-Aware Trajectory Optimization Under Uncertainty" (2016, 29 citations), introduces a novel problem formulation for planning continuous paths that maximize sensor coverage of a specified region while accounting for localization and sensing uncertainty—a critical challenge for autonomous exploration and surveillance tasks. Davis also contributed to augmented reality interfaces for robotics, as seen in his 2019 paper (6 citations), which addresses the gap between hardware advances and user interfaces for task-oriented robots. His work on "Multiworld Motion Planning" (2018, 2 citations) further pushes the boundaries of predictive planning by considering multiple distinct future outcomes rather than a single predicted scenario, offering a more robust framework for robot navigation in dynamic environments. Though his citation counts are modest, Davis's research represents important foundational steps in making autonomous systems more reliable and intuitive, particularly for field robotics applications where uncertainty is unavoidable.